import torch
from torch import nn

from basicsr.utils.registry import ARCH_REGISTRY


@ARCH_REGISTRY.register()
class AOD(nn.Module):
    def __init__(self):
        super().__init__()
        self.relu = nn.ReLU(inplace=True)

        self.e_conv1 = nn.Conv2d(3,3,1,1,0,bias=True)
        self.e_conv2 = nn.Conv2d(3,3,3,1,1,bias=True)
        self.e_conv3 = nn.Conv2d(6,3,5,1,2,bias=True)
        self.e_conv4 = nn.Conv2d(6,3,7,1,3,bias=True)
        self.e_conv5 = nn.Conv2d(12,3,3,1,1,bias=True)

    def forward(self, x):
        # source = []
        # source.append(x)

        x1 = self.relu(self.e_conv1(x))
        x2 = self.relu(self.e_conv2(x1))

        concat1 = torch.cat((x1,x2), 1)
        x3 = self.relu(self.e_conv3(concat1))

        concat2 = torch.cat((x2, x3), 1)
        x4 = self.relu(self.e_conv4(concat2))

        concat3 = torch.cat((x1,x2,x3,x4),1)
        x5 = self.relu(self.e_conv5(concat3))

        clean_image = self.relu((x5 * x) - x5 + 1)

        return clean_image
